Do as I steer, not as I say: Activation steering colluding Large Language Models to honesty
Jeroen Niemendal, Alessandro Castoldi, Sebastian Pusch, Sebastian Jitaru · Team The Colluders
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Large language model (LLM) agents can collude, covertly coordinating in favour of an outcome that harms the people a system is meant to serve. Collusion is hard to spot in the agents' text, but linear probes show it is represented in their hidden states. We ask whether this representation can be used to prevent collusion rather than only detect it. On NARCBench-Core, committees of four Qwen3-32B agents deliberate and vote between a fair option and a target that two agents are secretly instructed to promote. We estimate a collusion direction as the difference of mean activations between colluding and honest agents and refine it with a bi-directional preference objective. Subtracting this vector from the colluders' activations makes 22 of 24 vote for the fair option on held-out scenarios, against 1 of 24 without steering, while the unrefined difference-of-means vector flips at most 4 of 12. Most steered agents abandon collusion already during private coordination, but some keep arguing for the target and defect only at the final vote. Our results are a proof of concept that steering can override explicit collusive instructions.
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@misc{niemendal2026as,
title = {{Do as I steer, not as I say: Activation steering colluding Large Language Models to honesty}},
author = {Jeroen Niemendal and Alessandro Castoldi and Sebastian Pusch and Sebastian Jitaru},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/do-as-i-steer-not-as-i-say-activation-steering-colluding-large-language-models-to-honesty-1lbm}},
url = {https://apartresearch.com/sprints/projects/do-as-i-steer-not-as-i-say-activation-steering-colluding-large-language-models-to-honesty-1lbm}
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